<ul><li>Deep neural networks often lack interpretability, which limits their utility in fields like healthcare where transparency is crucial.</li><li>A new framework called Conformal Prediction for Interpretable Neural Networks (CONFINE) generates prediction sets with statistically robust uncertainty estimates.</li><li>CONFINE provides example-based explanations, confidence estimates, and improves accuracy by up to 3.6%.</li><li>CONFINE achieves a correct efficiency that is 3.3% higher than the original accuracy, demonstrating its validity across different tasks and surpassing previous methods.</li></ul>

CONFINE: Conformal Prediction for Interpretable Neural Networks

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